Multi-level hierarchical routing matrices for pattern-recognition processors

ABSTRACT

Multi-level hierarchical routing matrices for pattern-recognition processors are provided. One such routing matrix may include one or more programmable and/or non-programmable connections in and between levels of the matrix. The connections may couple routing lines to feature cells, groups, rows, blocks, or any other arrangement of components of the pattern-recognition processor.

CROSS-REFERENCE TO RELATED APPLICATIONS

The present application is a continuation of U.S. application Ser. No.12/638,759, entitled “Multi-Level Hierarchical Routing Matrices ForPattern-Recognition Processors,” and filed Dec. 15, 2009, now U.S. Pat.No. 9,323,994 which issued on Apr. 26, 2016, the entirety of which isincorporated by reference herein for all purposes.

BACKGROUND

Field of Invention

Embodiments of the invention relate generally to pattern-recognitionprocessors and, more specifically, in certain embodiments, to connectionarchitectures of such processors.

Description of Related Art

In the field of computing, pattern recognition tasks are increasinglychallenging. Ever larger volumes of data are transmitted betweencomputers, and the number of patterns that users wish to identify isincreasing. For example, spam or malware are often detected by searchingfor patterns in a data stream, e.g., particular phrases or pieces ofcode. The number of patterns increases with the variety of spam andmalware, as new patterns may be implemented to search for new variants.Searching a data stream for each of these patterns can form a computingbottleneck. Often, as the data stream is received, it is searched foreach pattern, one at a time. The delay before the system is ready tosearch the next portion of the data stream increases with the number ofpatterns. Thus, pattern recognition may slow the receipt of data.

Such patter-recognition processors may include a large number of finitestate machines (FSM) that move from state to state as inputs areprocessed. Internal connections of conventional processors rely onphysical wires connected to a flip-fop or other memory element. However,such connections may be incapable of meeting the performance for apattern-search processor. Further, such connections are generally notconfigurable or capable of meeting a desired functionality. Thedistance, speed, and configurability of the connections in apattern-recognition processor may be challenging to implement insilicon.

BRIEF DESCRIPTION OF DRAWINGS

FIG. 1 depicts an example of system that searches a data stream;

FIG. 2 depicts an example of a pattern-recognition processor in thesystem of FIG. 1;

FIG. 3 depicts an example of a search-term cell in thepattern-recognition processor of FIG. 2;

FIGS. 4 and 5 depict the search-term cell of FIG. 3 searching the datastream for a single character;

FIGS. 6-8 depict a recognition module including several search-termcells searching the data stream for a word;

FIG. 9 depicts the recognition module configured to search the datastream for two words in parallel;

FIGS. 10-12 depict the recognition module searching according to asearch criterion that specifies multiple words with the same prefix;

FIGS. 13-16 depict a hierarchical arrangement of feature cells of apattern-recognition processor in accordance with an embodiment of thepresent invention;

FIGS. 17-20 depict a multi-level hierarchical routing matrix of apattern-recognition processor in accordance with an embodiment of thepresent invention;

FIG. 21 depicts disabling of a portion of the feature cells of apattern-recognition processor in accordance with an embodiment of thepresent invention; and

FIG. 22 is a flowchart of a process for programming the connections of amulti-level hierarchical routing matrix in accordance with an embodimentof the present invention.

DETAILED DESCRIPTION

FIG. 1 depicts an example of a system 10 that searches a data stream 12.The system 10 may include a pattern-recognition processor 14 thatsearches the data stream 12 according to search criteria 16.

Each search criterion may specify one or more target expressions, i.e.,patterns. The phrase “target expression” refers to a sequence of datafor which the pattern-recognition processor 14 is searching. Examples oftarget expressions include a sequence of characters that spell a certainword, a sequence of genetic base pairs that specify a gene, a sequenceof bits in a picture or video file that form a portion of an image, asequence of bits in an executable file that form a part of a program, ora sequence of bits in an audio file that form a part of a song or aspoken phrase.

A search criterion may specify more than one target expression. Forexample, a search criterion may specify all five-letter words beginningwith the sequence of letters “cl”, any word beginning with the sequenceof letters “cl”, a paragraph that includes the word “cloud” more thanthree times, etc. The number of possible sets of target expressions isarbitrarily large, e.g., there may be as many target expressions asthere are permutations of data that the data stream could present. Thesearch criteria may be expressed in a variety of formats, including asregular expressions, a programming language that concisely specifiessets of target expressions without necessarily listing each targetexpression.

Each search criterion may be constructed from one or more search terms.Thus, each target expression of a search criterion may include one ormore search terms and some target expressions may use common searchterms. As used herein, the phrase “search term” refers to a sequence ofdata that is searched for, during a single search cycle. The sequence ofdata may include multiple bits of data in a binary format or otherformats, e.g., base ten, ASCII, etc. The sequence may encode the datawith a single digit or multiple digits, e.g., several binary digits. Forexample, the pattern-recognition processor 14 may search a text datastream 12 one character at a time, and the search terms may specify aset of single characters, e.g., the letter “a”, either the letters “a”or “e”, or a wildcard search term that specifies a set of all singlecharacters.

Search terms may be smaller or larger than the number of bits thatspecify a character (or other grapheme—i.e., fundamental unit—of theinformation expressed by the data stream, e.g., a musical note, agenetic base pair, a base-10 digit, or a sub-pixel). For instance, asearch term may be 8 bits and a single character may be 16 bits, inwhich case two consecutive search terms may specify a single character.

The search criteria 16 may be formatted for the pattern-recognitionprocessor 14 by a compiler 18. Formatting may include deconstructingsearch terms from the search criteria. For example, if the graphemesexpressed by the data stream 12 are larger than the search terms, thecompiler may deconstruct the search criterion into multiple search termsto search for a single grapheme. Similarly, if the graphemes expressedby the data stream 12 are smaller than the search terms, the compiler 18may provide a single search term, with unused bits, for each separategrapheme. The compiler 18 may also format the search criteria 16 tosupport various regular expressions operators that are not nativelysupported by the pattern-recognition processor 14.

The pattern-recognition processor 14 may search the data stream 12 byevaluating each new term from the data stream 12. The word “term” hererefers to the amount of data that could match a search term. During asearch cycle, the pattern-recognition processor 14 may determine whetherthe currently presented term matches the current search term in thesearch criterion. If the term matches the search term, the evaluation is“advanced”, i.e., the next term is compared to the next search term inthe search criterion. If the term does not match, the next term iscompared to the first term in the search criterion, thereby resettingthe search.

Each search criterion may be compiled into a different finite statemachine (FSM) in the pattern-recognition processor 14. The finite statemachines may run in parallel, searching the data stream 12 according tothe search criteria 16. The finite state machines may step through eachsuccessive search term in a search criterion as the preceding searchterm is matched by the data stream 12, or if the search term isunmatched, the finite state machines may begin searching for the firstsearch term of the search criterion.

The pattern-recognition processor 14 may evaluate each new termaccording to several search criteria, and their respective search terms,at about the same time, e.g., during a single device cycle. The parallelfinite state machines may each receive the term from the data stream 12at about the same time, and each of the parallel finite state machinesmay determine whether the term advances the parallel finite statemachine to the next search term in its search criterion. The parallelfinite state machines may evaluate terms according to a relatively largenumber of search criteria, e.g., more than 100, more than 1000, or morethan 10,000. Because they operate in parallel, they may apply the searchcriteria to a data stream 12 having a relatively high bandwidth, e.g., adata stream 12 of greater than or generally equal to 64 MB per second or128 MB per second, without slowing the data stream. In some embodiments,the search-cycle duration does not scale with the number of searchcriteria, so the number of search criteria may have little to no effecton the performance of the pattern-recognition processor 14.

When a search criterion is satisfied (i.e., after advancing to the lastsearch term and matching it), the pattern-recognition processor 14 mayreport the satisfaction of the criterion to a processing unit, such as acentral processing unit (CPU) 20. The central processing unit 20 maycontrol the pattern-recognition processor 14 and other portions of thesystem 10.

The system 10 may be any of a variety of systems or devices that searcha stream of data. For example, the system 10 may be a desktop, laptop,handheld or other type of computer that searches the data stream 12. Thesystem 10 may also be a network node, such as a router, a server, or aclient (e.g., one of the previously-described types of computers). Thesystem 10 may be some other sort of electronic device, such as a copier,a scanner, a printer, a game console, a television, a set-top videodistribution or recording system, a cable box, a personal digital mediaplayer, a factory automation system, an automotive computer system, or amedical device. (The terms used to describe these various examples ofsystems, like many of the other terms used herein, may share somereferents and, as such, should not be construed narrowly in virtue ofthe other items listed.)

The data stream 12 may be one or more of a variety of types of datastreams that a user or other entity might wish to search. For example,the data stream 12 may be a stream of data received over a network, suchas packets received over the Internet or voice or data received over acellular network. The data stream 12 may be data received from a sensorin communication with the system 10, such as an imaging sensor, atemperature sensor, an accelerometer, or the like, or combinationsthereof. The data stream 12 may be received by the system 10 as a serialdata stream, in which the data is received in an order that has meaning,such as in a temporally, lexically, or semantically significant order.Or the data stream 12 may be received in parallel or out of order and,then, converted into a serial data stream, e.g., by reordering packetsreceived over the Internet. In some embodiments, the data stream 12 maypresent terms serially, but the bits expressing each of the terms may bereceived in parallel. The data stream 12 may be received from a sourceexternal to the system 10, or may be formed by interrogating a memorydevice and forming the data stream 12 from stored data.

Depending on the type of data in the data stream 12, different types ofsearch criteria may be chosen by a designer. For instance, the searchcriteria 16 may be a virus definition file. Viruses or other malware maybe characterized, and aspects of the malware may be used to form searchcriteria that indicate whether the data stream 12 is likely deliveringmalware. The resulting search criteria may be stored on a server, and anoperator of a client system may subscribe to a service that downloadsthe search criteria to the system 10. The search criteria 16 may beperiodically updated from the server as different types of malwareemerge. The search criteria may also be used to specify undesirablecontent that might be received over a network, for instance unwantedemails (commonly known as spam) or other content that a user findsobjectionable.

The data stream 12 may be searched by a third party with an interest inthe data being received by the system 10. For example, the data stream12 may be searched for text, a sequence of audio, or a sequence of videothat occurs in a copyrighted work. The data stream 12 may be searchedfor utterances that are relevant to a criminal investigation or civilproceeding or are of interest to an employer. In other embodiments,monitoring a data stream for data of interest may be an example ofsearching.

The search criteria 16 may also include patterns in the data stream 12for which a translation is available, e.g., in memory addressable by theCPU 20 or the pattern-recognition processor 14. For instance, the searchcriteria 16 may each specify an English word for which a correspondingSpanish word is stored in memory. In another example, the searchcriteria 16 may specify encoded versions of the data stream 12, e.g.,MP3, MPEG 4, FLAC, Ogg Vorbis, etc., for which a decoded version of thedata stream 12 is available, or vice versa.

The pattern-recognition processor 14 may be hardware that is integratedwith the CPU 20 into a single component (such as a single device) or maybe formed as a separate component. For instance, the pattern-recognitionprocessor 14 may be a separate integrated circuit. Thepattern-recognition processor 14 may be referred to as a “co-processor”or a “pattern-recognition co-processor”.

FIG. 2 depicts an example of the pattern-recognition processor 14. Thepattern-recognition processor 14 may include a recognition module 22 andan aggregation module 24. The recognition module 22 may be configured tocompare received terms to search terms, and both the recognition module22 and the aggregation module 24 may cooperate to determine whethermatching a term with a search term satisfies a search criterion.

The recognition module 22 may include a row decoder 28 and a pluralityof feature cells 30. Each feature cell 30 may specify a search term, andgroups of feature cells 30 may form a parallel finite state machine thatforms a search criterion. Components of the feature cells 30 may form asearch-term array 32, a detection array 34, and an activation-routingmatrix 36. The search-term array 32 may include a plurality of inputconductors 37, each of which may place each of the feature cells 30 incommunication with the row decoder 28.

The row decoder 28 may select particular conductors among the pluralityof input conductors 37 based on the content of the data stream 12. Forexample, the row decoder 28 may be a one byte to 256 row decoder thatactivates one of 256 rows based on the value of a received byte, whichmay represent one term. A one-byte term of 0000 0000 may correspond tothe top row among the plurality of input conductors 37, and a one-byteterm of 1111 1111 may correspond to the bottom row among the pluralityof input conductors 37. Thus, different input conductors 37 may beselected, depending on which terms are received from the data stream 12.As different terms are received, the row decoder 28 may deactivate therow corresponding to the previous term and activate the rowcorresponding to the new term.

The detection array 34 may couple to a detection bus 38 that outputssignals indicative of complete or partial satisfaction of searchcriteria to the aggregation module 24. The activation-routing matrix 36may selectively activate and deactivate feature cells 30 based on, forexample, the number of search terms in a search criterion that have beenmatched.

The aggregation module 24 may include a latch matrix 40, anaggregation-routing matrix 42, a threshold-logic matrix 44, alogical-product matrix 46, a logical-sum matrix 48, and aninitialization-routing matrix 50.

The latch matrix 40 may implement portions of certain search criteria.Some search criteria, e.g., some regular expressions, count only thefirst occurrence of a match or group of matches. The latch matrix 40 mayinclude latches that record whether a match has occurred. The latchesmay be cleared during initialization, and periodically re-initializedduring operation, as search criteria are determined to be satisfied ornot further satisfiable—i.e., an earlier search term may need to bematched again before the search criterion could be satisfied.

The aggregation-routing matrix 42 may function similar to theactivation-routing matrix 36. The aggregation-routing matrix 42 mayreceive signals indicative of matches on the detection bus 38 and mayroute the signals to different group-logic lines 53 connecting to thethreshold-logic matrix 44. The aggregation-routing matrix 42 may alsoroute outputs of the initialization-routing matrix 50 to the detectionarray 34 to reset portions of the detection array 34 when a searchcriterion is determined to be satisfied or not further satisfiable.

The threshold-logic matrix 44 may include a plurality of counters, e.g.,32-bit counters configured to count up or down. The threshold-logicmatrix 44 may be loaded with an initial count, and it may count up ordown from the count based on matches signaled by the recognition module.For instance, the threshold-logic matrix 44 may count the number ofoccurrences of a word in some length of text.

The outputs of the threshold-logic matrix 44 may be inputs to thelogical-product matrix 46. The logical-product matrix 46 may selectivelygenerate “product” results (e.g., “AND” function in Boolean logic). Thelogical-product matrix 46 may be implemented as a square matrix, inwhich the number of output products is equal the number of input linesfrom the threshold-logic matrix 44, or the logical-product matrix 46 mayhave a different number of inputs than outputs. The resulting productvalues may be output to the logical-sum matrix 48.

The logical-sum matrix 48 may selectively generate sums (e.g., “OR”functions in Boolean logic.) The logical-sum matrix 48 may also be asquare matrix, or the logical-sum matrix 48 may have a different numberof inputs than outputs. Since the inputs are logical products, theoutputs of the logical-sum matrix 48 may be logical-Sums-of-Products(e.g., Boolean logic Sum-of-Product (SOP) form). The output of thelogical-sum matrix 48 may be received by the initialization-routingmatrix 50.

The initialization-routing matrix 50 may reset portions of the detectionarray 34 and the aggregation module 24 via the aggregation-routingmatrix 42. The initialization-routing matrix 50 may also be implementedas a square matrix, or the initialization-routing matrix 50 may have adifferent number of inputs than outputs. The initialization-routingmatrix 50 may respond to signals from the logical-sum matrix 48 andre-initialize other portions of the pattern-recognition processor 14,such as when a search criterion is satisfied or determined to be notfurther satisfiable.

The aggregation module 24 may include an output buffer 51 that receivesthe outputs of the threshold-logic matrix 44, the aggregation-routingmatrix 42, and the logical-sum matrix 48. The output of the aggregationmodule 24 may be transmitted from the output buffer 51 to the CPU 20(FIG. 1) on the output bus 26. In some embodiments, an outputmultiplexer may multiplex signals from these components 42, 44, and 48and output signals indicative of satisfaction of criteria or matches ofsearch terms to the CPU 20 (FIG. 1). In other embodiments, results fromthe pattern-recognition processor 14 may be reported withouttransmitting the signals through the output multiplexer, which is not tosuggest that any other feature described herein could not also beomitted. For example, signals from the threshold-logic matrix 44, thelogical-product matrix 46, the logical-sum matrix 48, or theinitialization routing matrix 50 may be transmitted to the CPU inparallel on the output bus 26.

FIG. 3 illustrates a portion of a single feature cell 30 in thesearch-term array 32 (FIG. 2), a component referred to herein as asearch-term cell 54. The search-term cells 54 may include an outputconductor 56 and a plurality of memory cells 58. Each of the memorycells 58 may be coupled to both the output conductor 56 and one of theconductors among the plurality of input conductors 37. In response toits input conductor 37 being selected, each of the memory cells 58 mayoutput a value indicative of its stored value, outputting the datathrough the output conductor 56. In some embodiments, the plurality ofinput conductors 37 may be referred to as “word lines”, and the outputconductor 56 may be referred to as a “data line”.

The memory cells 58 may include any of a variety of types of memorycells. For example, the memory cells 58 may be volatile memory, such asdynamic random access memory (DRAM) cells having a transistor and acapacitor. The source and the drain of the transistor may be connectedto a plate of the capacitor and the output conductor 56, respectively,and the gate of the transistor may be connected to one of the inputconductors 37. In another example of volatile memory, each of the memorycells 58 may include a static random access memory (SRAM) cell. The SRAMcell may have an output that is selectively coupled to the outputconductor 56 by an access transistor controlled by one of the inputconductors 37. The memory cells 58 may also include nonvolatile memory,such as phase-change memory (e.g., an ovonic device), flash memory,silicon-oxide-nitride-oxide-silicon (SONOS) memory, magneto-resistivememory, or other types of nonvolatile memory. The memory cells 58 mayalso include flip-flops, e.g., memory cells made out of logic gates.

FIGS. 4 and 5 depict an example of the search-term cell 54 in operation.FIG. 4 illustrates the search-term cell 54 receiving a term that doesnot match the cell's search term, and FIG. 5 illustrates a match.

As illustrated by FIG. 4, the search-term cell 54 may be configured tosearch for one or more terms by storing data in the memory cells 58. Thememory cells 58 may each represent a term that the data stream 12 mightpresent, e.g., in FIG. 3, each memory cell 58 represents a single letteror number, starting with the letter “a” and ending with the number “9”.Memory cells 58 representing terms that satisfy the search term may beprogrammed to store a first value, and memory cells 58 that do notrepresent terms that satisfy the search term may be programmed to storea different value. In the illustrated example, the search-term cell 54is configured to search for the letter “b”. The memory cells 58 thatrepresent “b” may store a 1, or logic high, and the memory cells 58 thatdo not represent “b” may be programmed to store a 0, or logic low.

To compare a term from the data stream 12 with the search term, the rowdecoder 28 may select the input conductor 37 coupled to memory cells 58representing the received term. In FIG. 4, the data stream 12 presents alowercase “e”. This term may be presented by the data stream 12 in theform of an eight-bit ASCII code, and the row decoder 28 may interpretthis byte as a row address, outputting a signal on the conductor 60 byenergizing it.

In response, the memory cell 58 controlled by the conductor 60 mayoutput a signal indicative of the data that the memory cell 58 stores,and the signal may be conveyed by the output conductor 56. In this case,because the letter “e” is not one of the terms specified by thesearch-term cell 54, it does not match the search term, and thesearch-term cell 54 outputs a 0 value, indicating no match was found.

In FIG. 5, the data stream 12 presents a character “b”. Again, the rowdecoder 28 may interpret this term as an address, and the row decoder 28may select the conductor 62. In response, the memory cell 58representing the letter “b” outputs its stored value, which in this caseis a 1, indicating a match.

The search-term cells 54 may be configured to search for more than oneterm at a time. Multiple memory cells 58 may be programmed to store a 1,specifying a search term that matches with more than one term. Forinstance, the memory cells 58 representing the letters lowercase “a” anduppercase “A” may be programmed to store a 1, and the search-term cell54 may search for either term. In another example, the search-term cell54 may be configured to output a match if any character is received. Allof the memory cells 58 may be programmed to store a 1, such that thesearch-term cell 54 may function as a wildcard term in a searchcriterion.

FIGS. 6-8 depict the recognition module 22 searching according to amulti-term search criterion, e.g., for a word. Specifically, FIG. 6illustrates the recognition module 22 detecting the first letter of aword, FIG. 7 illustrates detection of the second letter, and FIG. 8illustrates detection of the last letter.

As illustrated by FIG. 6, the recognition module 22 may be configured tosearch for the word “big”. Three adjacent feature cells 63, 64, and 66are illustrated. The feature cell 63 is configured to detect the letter“b”. The feature cell 64 is configured to detect the letter “i”. And thefeature cell 66 is configured to both detect the letter “g” and indicatethat the search criterion is satisfied.

FIG. 6 also depicts additional details of the detection array 34. Thedetection array 34 may include a detection cell 68 in each of thefeature cells 63, 64, and 66. Each of the detection cells 68 may includea memory cell 70, such as one of the types of memory cells describedabove (e.g., a flip-flop), that indicates whether the feature cell 63,64, or 66 is active or inactive. The detection cells 68 may beconfigured to output a signal to the activation-routing matrix 36indicating whether the detection cell both is active and has received asignal from its associated search-term cell 54 indicating a match.Inactive features cells 63, 64, and 66 may disregard matches. Each ofthe detection cells 68 may include an AND gate with inputs from thememory cell 70 and the output conductor 56. The output of the AND gatemay be routed to both the detection bus 38 and the activation-routingmatrix 36, or one or the other.

The activation-routing matrix 36, in turn, may selectively activate thefeature cells 63, 64, and 66 by writing to the memory cells 70 in thedetection array 34. The activation-routing matrix 36 may activatefeature cells 63, 64, or 66 according to the search criterion and whichsearch term is being searched for next in the data stream 12.

In FIG. 6, the data stream 12 presents the letter “b”. In response, eachof the feature cells 63, 64, and 66 may output a signal on their outputconductor 56, indicating the value stored in the memory cell 58connected to the conductor 62, which represents the letter “b”. Thedetection cells 56 may then each determine whether they have received asignal indicating a match and whether they are active. Because thefeature cell 63 is configured to detect the letter “b” and is active, asindicated by its memory cell 70, the detection cell 68 in the featurecell 63 may output a signal to the activation-routing matrix 36indicating that the first search term of the search criterion has beenmatched.

As illustrated by FIG. 7, after the first search term is matched, theactivation-routing matrix 36 may activate the next feature cell 64 bywriting a 1 to its memory cell 70 in its detection cell 68. Theactivation-routing matrix 36 may also maintain the active state of thefeature cell 63, in case the next term satisfies the first search term,e.g., if the sequence of terms “bbig” is received. The first search termof search criteria may be maintained in an active state during a portionor substantially all of the time during which the data stream 12 issearched.

In FIG. 7, the data stream 12 presents the letter “i” to the recognitionmodule 22. In response, each of the feature cells 63, 64, and 66 mayoutput a signal on their output conductor 56, indicating the valuestored in the memory cell 58 connected to the conductor 72, whichrepresents the letter “i”. The detection cells 56 may then eachdetermine whether they have received a signal indicating a match andwhether they are active. Because the feature cell 64 is configured todetect the letter “i” and is active, as indicated by its memory cell 70,the detection cell 68 in the feature cell 64 may output a signal to theactivation-routing matrix 36 indicating that the next search term of itssearch criterion has been matched.

Next, the activation-routing matrix 36 may activate the feature cell 66,as illustrated by FIG. 8. Before evaluating the next term, the featurecell 64 may be deactivated. The feature cell 64 may be deactivated byits detection cell 68 resetting its memory cell 70 between detectioncycles or the activation-routing matrix 36 may deactivate the featurecell 64, for example.

In FIG. 8, the data stream 12 presents the term “g” to the row decoder28, which selects the conductor 74 representing the term “g”. Inresponse, each of the feature cells 63, 64, and 66 may output a signalon their output conductor 56, indicating the value stored in the memorycell 58 connected to the conductor 74, which represents the letter “g”.The detection cells 56 may then each determine whether they havereceived a signal indicating a match and whether they are active.Because the feature cell 66 is configured to detect the letter “g” andis active, as indicated by its memory cell 70, the detection cell 68 inthe feature cell 66 may output a signal to the activation routing matrix36 indicating that the last search term of its search criterion has beenmatched.

The end of a search criterion or a portion of a search criterion may beidentified by the activation-routing matrix 36 or the detection cell 68.These components 36 or 68 may include memory indicating whether theirfeature cell 63, 64, or 66 specifies the last search term of a searchcriterion or a component of a search criterion. For example, a searchcriterion may specify all sentences in which the word “cattle” occurstwice, and the recognition module may output a signal indicating eachoccurrence of “cattle” within a sentence to the aggregation module,which may count the occurrences to determine whether the searchcriterion is satisfied.

Feature cells 63, 64, or 66 may be activated under several conditions. Afeature cell 63, 64, or 66 may be “always active”, meaning that itremains active during all or substantially all of a search. An exampleof an always active feature cell 63, 64, or 66 is the first feature cellof the search criterion, e.g., feature cell 63.

A feature cell 63, 64, or 66 may be “active when requested”, meaningthat the feature cell 63, 64, or 66 is active when some conditionprecedent is matched, e.g., when the preceding search terms in a searchcriterion are matched. An example is the feature cell 64, which isactive when requested by the feature cell 63 in FIGS. 6-8, and thefeature cell 66, which active when requested by the feature cell 64.

A feature cell 63, 64, or 66 may be “self activated”, meaning that onceit is activated, it activates itself as long as its search term ismatched. For example, a self activated feature cell having a search termthat is matched by any numerical digit may remain active through thesequence “123456xy” until the letter “x” is reached. Each time thesearch term of the self activated feature cell is matched, it mayactivate the next feature cell in the search criterion. Thus, an alwaysactive feature cell may be formed from a self activating feature celland an active when requested feature cell: the self activating featurecell may be programmed with all of its memory cells 58 storing a 1, andit may repeatedly activate the active when requested feature cell aftereach term. In some embodiments, each feature cell 63, 64, and 66 mayinclude a memory cell in its detection cell 68 or in theactivation-routing matrix 36 that specifies whether the feature cell isalways active, thereby forming an always active feature cell from asingle feature cell.

FIG. 9 depicts an example of a recognition module 22 configured tosearch according to a first search criterion 75 and a second searchcriterion 76 in parallel. In this example, the first search criterion 75specifies the word “big”, and the second search criterion 76 specifiesthe word “cab”. A signal indicative of the current term from the datastream 12 may be communicated to feature cells in each search criterion75 and 76 at generally the same time. Each of the input conductors 37spans both of the search criteria 75 and 76. As a result, in someembodiments, both of the search criteria 75 and 76 may evaluate thecurrent term generally simultaneously. This is believed to speed theevaluation of search criteria. Other embodiments may include morefeature cells configured to evaluate more search criteria in parallel.For example, some embodiments may include more than 100, 500, 1000,5000, or 10,000 feature cells operating in parallel. These feature cellsmay evaluate hundreds or thousands of search criteria generallysimultaneously.

Search criteria with different numbers of search terms may be formed byallocating more or fewer feature cells to the search criteria. Simplesearch criteria may consume fewer resources in the form of feature cellsthan complex search criteria. This is believed to reduce the cost of thepattern-recognition processor 14 (FIG. 2) relative to processors with alarge number of generally identical cores, all configured to evaluatecomplex search criteria.

FIGS. 10-12 depict both an example of a more complex search criterionand features of the activation-routing matrix 36. The activation-routingmatrix 36 may include a plurality of activation-routing cells 78, groupsof which may be associated with each of the feature cells 63, 64, 66,80, 82, 84, and 86. For instance, each of the feature cells may include5, 10, 20, 50, or more activation-routing cells 78. Theactivation-routing cells 78 may be configured to transmit activationsignals to the next search term in a search criterion when a precedingsearch term is matched. The activation-routing cells 78 may beconfigured to route activation signals to adjacent feature cells orother activation-routing cells 78 within the same feature cell. Theactivation-routing cells 78 may include memory that indicates whichfeature cells correspond to the next search term in a search criterion.

As illustrated by FIGS. 10-12, the recognition module 22 may beconfigured to search according to complex search criteria than criteriathat specify single words. For instance, the recognition module 22 maybe configured to search for words beginning with a prefix 88 and endingwith one of two suffixes 90 or 92. The illustrated search criterionspecifies words beginning with the letters “c” and “l” in sequence andending with either the sequence of letters “op” or the sequence ofletters “oud”. This is an example of a search criterion specifyingmultiple target expressions, e.g., the word “clap” or the word “cloud”.

In FIG. 10, the data stream 12 presents the letter “c” to therecognition module 22, and feature cell 63 is both active and detects amatch. In response, the activation-routing matrix 36 may activate thenext feature cell 64. The activation-routing matrix 36 may also maintainthe active state of the feature cell 63, as the feature cell 63 is thefirst search term in the search criterion.

In FIG. 11, the data stream 12 presents a letter “l”, and the featurecell 64 recognizes a match and is active. In response, theactivation-routing matrix 36 may transmit an activation signal both tothe first feature cell 66 of the first suffix 90 and to the firstfeature cell 82 of the second suffix 92. In other examples, moresuffixes may be activated, or multiple prefixes may active one or moresuffixes.

Next, as illustrated by FIG. 12, the data stream 12 presents the letter“o” to the recognition module 22, and the feature cell 82 of the secondsuffix 92 detects a match and is active. In response, theactivation-routing matrix 36 may activate the next feature cell 84 ofthe second suffix 92. The search for the first suffix 90 may die out, asthe feature cell 66 is allowed to go inactive. The steps illustrated byFIGS. 10-12 may continue through the letters “u” and “d”, or the searchmay die out until the next time the prefix 88 is matched.

Embodiments of the pattern recognition processor 14 may include anyarrangement of feature cells 30. FIGS. 13-16 depict a hierarchicalarrangement of feature cells 30 in accordance with an embodiment of thepresent invention. In one embodiment, as depicted in FIG. 13, a firstlevel of a hierarchy may include the feature cells 30 arranged in groups94 of two feature cells 30, Feature Cell 1 and Feature Cell 0. Eachfeature cell 30 may receive an input, e.g., an Enable State signal, andmay output a Next State signal to another group of feature cells. Eachfeature cell 30 in the group 94 may be coupled to an output drive select96 that provides an output from the group 94 based on the outputs ofeach feature cell 30. For example, in one embodiment, the output driveselect 96 may be configured to output a Next State Out “0” signal, aNext State Out “1” signal, or the logical OR of the two Next State Outsignals received from the feature cells 30.

As shown in FIG. 14, a second hierarchical level may include each group94 of feature cells arranged in a row 98 of groups 94. Each row 98 mayinclude any number of groups 94 of feature cells 30. For example, in theembodiment shown in FIG. 14, the row 98 may include eight groups 94 oftwo feature cells 30, e.g. Group 0 through Group 7.

As shown in FIG. 15, a third level of a hierarchy may include multiplerows 98 grouped into blocks 100, wherein each block 100 includes one ormore rows 98. In one embodiment of the processor 14, each block 100 mayinclude 16 rows 98, e.g., Row 0 through Row 15. The pattern-recognitionprocessor 14 may then include any number of blocks 100 for implementingthe programmed state machines and pattern searching described above. Asshown in FIG. 16, in one embodiment the pattern-recognition processor 14may include 512 blocks, e.g., Block 0 through Block 512.

The groups 94, rows 98, and blocks 100 illustrated above describe ahierarchical arrangement of the feature cells. A programmed statemachine may include any number of feature cells 30. Thus, each group,row, or block may include multiple programmed state machines. Duringoperation of the pattern-recognition processor 14, such as during thesearch cycle described above, each state of a programmed state machine(e.g., one or more feature cells) is routed to the next state of theprogrammed state machine (referred to as “next state routing”), by theNext State signals output from each feature cell 30 and selected by theoutput drive select 96 of each group 94.

FIGS. 17-21 describe a multi-level hierarchical routing matrix thatprovides next state routing, programmability, and high throughput, inaccordance with an embodiment of the present invention. As used herein,the term “routing matrix” refers to a plurality of connections forrouting communications between components of the pattern-recognitionprocessor 14. The “routing matrix” described below can be functionallydistinct from the matrices described above in FIGS. 1-12. As describedfurther below, the routing matrix can provide for programmable and/ornon-programmable connections at, in, and between every level of thehierarchy of the pattern-recognition processor 14 described above. Theconnections may connect routing lines between feature cells, groups,rows, and blocks of the pattern-recognition processor 14. Theconnections may include, but are not limited to, the following types ofconnections: programmable and non-programmable; uni-directional andbi-directional; logical combinations (OR, AND, XOR, etc.); selectors(e.g., one of many); and isolators (break connections to a line). Aprogrammable connection may be configured to perform any of thefunctionality listed above. For example, a programmable connection maybe programmed as uni-directional, bi-directional, any logicalcombination, selector, isolator, etc. A non-programmable connection mayperform any of the functionality described above, but is incapable ofbeing programmed with a different functionality.

The connections in FIGS. 17-21 are depicted by connection symbolssummarized below in Table 1:

TABLE 1 Connections Symbol Description ● Non-programmable “1^(st) level”output connection ◯ Programmable “1^(st) level” input connection □Programmable “2^(nd) level” input connection □ Programmable “2^(nd)level” output connection

Programmable “3^(rd) level” connection

Programmable “4^(th) level” connection

FIG. 17 depicts a hierarchy level that includes the group 94 of featurecells 30 described above in FIG. 13 and in accordance with an embodimentof the present invention. As mentioned above, each feature cell 30 mayreceive an input that enables the feature cell as the next state. Asalso mentioned above, based on the pattern matching executed against thesearch criteria programmed in the feature cells 30, the feature cell 30may generate an output that enables the next active state (next statesignal).

The routing of the input and output signals of the feature cells 30 aredetermined by the connections. The feature cells 30 of the group 94 maybe interconnected by local route lines 102 (Local Route 0 and LocalRoute 1). The outputs of the feature cells 30 of the group 94 arecoupled to the local route lines 102 and the output drive select 96 byoutput connections 104. For example, Feature Cell 0 is coupled to LocalRoute Line 0 by a first output connection 104A and Feature Cell 1 iscoupled to Local Route Line 1 by a second output connection 104B. Asdepicted in FIG. 17, in one embodiment, the output connections arenon-programmable “1^(st) Level” connections. In such an embodiment, theconnections 104 are not removable and are not configurable. In otherembodiments, the output connections 104 may be programmable.

The output drive select 96 may be programmed to drive any number or typeof signals from the received outputs of the feature cells 30. Asmentioned above, in one embodiment, the output drive select 96 may beconfigured to output one of three possible logical outputs: “Next StateOut 0”; “Next State Out 1”; or the logical OR of the two Next State Outsignals. In other embodiments, the output drive select 96 may beconfigured to output other logical combinations, such as AND, NOR and/orXOR.

The local route lines 102 may be coupled to the inputs 105 (which mayrepresent one or more input signals) of the feature cells 30, by inputconnections 106. For example, the Feature Cell 0 may be coupled to LocalRoute Lines 0 and 1 by input connections 106A and 106B respectively.Similarly, Feature Cell 1 may be coupled to Local Route Line 0 and LocalRoute Line 1 by input connections 106C and 106D respectively. Asdepicted in FIG. 17, the input connections 106 may be programmable“1^(st) Level” connections. In such an embodiment, the input connections106 may be configured to provide a logical OR of any of the connectedinputs 105.

FIG. 18 depicts a hierarchy level having the row 98 of the groups 94 asdescribed above in FIG. 14 and in accordance with an embodiment of thepresent invention. As mentioned above, each row 98 may include anynumber of groups 94 of feature cells 30, e.g., Group 0 through Group 7shown in FIG. 18. The groups of the row 98 may be interconnected by rowroute lines 108. In one embodiment, row route lines 108 may be providedfor each row of the block 100. Thus, in an embodiment having 16 rows 98per block 100, 16 row route lines may be provided, e.g., Row Route line0 through Row Route line 15.

The outputs from the output drive selects 96 of each group 94 may becoupled to each row route line 108 by output connections 110. In oneembodiment, the output connections may be programmable “2^(nd) level”connections. As shown in FIG. 18, for example, Group 0 may be coupled tothe row route lines 0 and 15 by output connections 110A and 110Brespectively. Group 7 may be coupled to the row route lines 0 and 15 byoutput connections 110C and 110D respectively. All other row route lines(not shown) may be coupled to the output drive selects of Groups 0through Group 7 by output connections 110. The output connections 110may be configured to enable the output drive select 96 of a group 94 todrive or not drive a particular row route line 108.

Additionally, the row route lines 108 may be coupled to the inputs 105of each feature cell 30 by input connections 112. In one embodiment, theinput connections 112 may be programmable “2^(nd) level” connections.For example, the row route lines 108 may be coupled to the inputs ofFeature Cell 0 of Group 0 by input connections 112A and 112B, and therow route lines 108 may be coupled to the inputs of Feature Cell 1 ofGroup 0 by input connections 112C and 112D. Similarly, as also shown inFIG. 18, the row route lines 108 may be coupled to the inputs of FeatureCell 0 of Group 7 by input connections 112E and 112F, and the row routelines 108 may be coupled to Feature Cell 1 of Group 7 by inputconnections 112G and 112H. Other row route lines (not shown) may becoupled to the inputs of each feature cell 30 of each group 94 of therow 98. In such an embodiment, the input connections 112 may beprogrammed to be a logical OR of any connected inputs to the featurecells 30. In other embodiments, the connections may be non-programmableand/or bi-directional connections.

Next, FIG. 19 depicts a hierarchy level of a block 100 having multiplerows 98 as described above in FIG. 15 and in accordance with anembodiment of the present invention. As described above, the block 100may include any number of rows 98, e.g., Row 0 through Row 15. The rows98 of the block 100 may be connected by intra-block route lines 114. Theintra-block route lines 114 may be coupled to the row route lines 112 bybi-directional connections 116. In one embodiment, the bi-directionalconnections may be programmable “3^(rd) level” connections. For example,intra-block line route line 0 may be coupled to Row Route line 0 of Row0 by bi-directional connection 116A and Row Route line 15 of Row 0 bybi-directional connection 116B. Intra-block line route line 0 may becoupled to Row Route line 0 of Row 15 by bi-directional connections 116Cand Row Route line 15 of Row 15 by bi-directional connection 116D.Similarly, intra-block route line 23 may be coupled to Row Route line 0of Row 0 by bi-directional connection 116E and Row Route line 15 of Row0 by bi-directional connection 116F. Further, as also shown in FIG. 19,intra-block route line 23 may be coupled to Row Route line 0 of Row 15by bi-directional connections 116G and Row Route line 15 of Row 15 bybi-directional connection 116H. Other intra block lines (not shown) maybe coupled to each row route line 116 of each row 98 by bi-directionalconnections 116.

As described above, the bi-directional connections 116 may beprogrammable. Thus, the bi-directional connections 116 may be programmedto enable one or more of the intra-block route lines 114 to drive arespective row route line 112 or to enable one or more row route lines112 to drive a respective intra block route line 114. Eachbi-directional connection 116 may be individually programmed, enablingconfiguration of connections between row route lines 112 and intra-blockroute lines 114 on a line-by-line basis. In other embodiments, theconnections may be non-programmable and/or uni-directional connections.

FIG. 20 depicts a top hierarchy level 117 of the routing matrix havingblocks 100 in accordance with an embodiment of the present invention. Inone embodiment, as shown in FIG. 20, the top level 117 may include 512blocks 100, e.g., Block 0 through Block 511. The blocks 100 may beinterconnected by top level route lines 118. The top level route lines118 may be connected to the intra-block route lines 114 bybi-directional connections 120. Thus, as shown in FIG. 20, top levelroute line 0 may be coupled to intra-block route line 0 and intra-blockroute line 23 of Block 0 by bi-directional connections 120A and 120Brespectively. Similarly, top level route line 0 may be coupled tointra-block line 0 and intra-block line 23 of Block 511 bybi-directional connections 120C and 120D respectively. As shown in FIG.20, top level route line 23 may be coupled to intra-block route line 0and intra-block route line 23 of Block 0 by bi-directional connections120E and 120F respectively. Further, top level route line 23 may becoupled to intra-block line 0 and intra-block line 23 of Block 511 bybi-directional connections 120G and 120H respectively. All other toplevel route lines (not shown) may be coupled to the intra-block lines114 of the blocks 100 by bi-directional connections 120.

As shown in FIG. 20, the bi-directional connections 120 may beprogrammable “4^(th) level connections.” The bi-directional connectionsmay be programmed to enable one or more intra-block route lines 114 todrive a respective top level route line 118 or to enable one or more toplevel route lines 118 to drive a respective intra-block route line 114.Thus, the connections 120 may be programmed and configured on aline-by-line basis. In other embodiments, the connections may benon-programmable and/or uni-directional connections.

Advantageously, the multi-level hierarchical routing matrix describedabove may provide regularity of the programmability of the device,implementation of redundancy for improvements in production andmanufacturing yields, changeability for different applications, andeasier visualization and implementation of logic changes.

As mentioned above, the connections may be isolators that are capable of“breaking” a line such that no signals are routed over a line, enablinga redundant section of the pattern-recognition processor 14 to bedisabled. FIG. 21 depicts isolation of one or more feature cells 30 ofthe pattern-recognition processor 14 in accordance with an embodiment ofthe present invention. For example, the pattern-recognition processor 14may include a block 130 of feature cells 30 that provide more capacitythan used by the pattern-recognition processor 14. That is, duringmanufacture, to increase yields a pattern-recognition processor 14 maybe manufactured with excess memory capacity (excess feature cells 30)than specified for the function of the processor 14. Duringmanufacturing and testing of the processor 14, the excess feature cells30 may be “disabled” by removing the feature cells available for theprogrammed state machines used by the processor 14. Some embodiments maynot use all of the blocks of feature cells 30. In such embodiments, theunused blocks may be disabled. Disabled blocks may not be “activated”and/or “powered-up,” and may not be refreshed during refresh cycles.

The block 130 may be coupled to the other portions of thepattern-recognition processor 14 by the connections 132 between the toplevel route lines and the intra-block route lines. In such anembodiment, the connections 132 may be programmable “4^(th) levelconnections” that may be programmed to any desired functionality.Accordingly, if block 130 provides excess capacity, the connections 132may be programmed to isolate block 130 from the rest of the route lines.Thus, the connection between the top level route lines 118 and theintra-block routing lines 114 may be “broken” by the programmableconnections 132. Block 130 may be referred to as “disabled.” Inaddition, unused block 130 may be “powered down,” such as by setting anappropriate programming bit, in the block 130.

In contrast, other blocks that are used to provide memory capacity forprogrammed state machines of the pattern-recognition processor 14 may beaccessible through the top level route lines 118 and intra-block routelines 114. For example, as shown in FIG. 21, block 134 is also connectedto the same top level route lines 118 as disabled block 132, byconnections 136. As shown in FIG. 21, the connections 136 may beprogrammable 4^(th) level connections. However, the connections 136 maybe programmed to enable access to the memory capacity provided by block134, such as by allowing the top level route lines to drive theintra-block route lines or vice-versa. In other embodiments, featurecells 30 may be disabled at the row level, such as by programming theconnections 138 between the intra-block route lines 114 and the rowroute lines 112, and/or at the group level, such as by programming theconnections 140 between the row route lines 112 and the local routelines 102.

Further, in other embodiments, the multi-level hierarchical routingmatrix described above may vary in levels, connections, etc., based onthe pattern-matching functionality implemented in thepattern-recognition processor 14. For example, other embodiments mayinclude a different number of levels in the hierarchy, and/or differentnumber of connections between levels, groups, rows, and/or blocks.Additionally, other embodiments may include different programmablefunctions usable for the programmable connections, different types ofconnections and different points in the hierarchy, the ability toprogrammatically break connection lines into multiple lines, and theability to add and/or delete different functionality at different levelsin the hierarchy.

FIG. 22 depicts a process 142 of configuration of the multi-levelhierarchical routing matrix described above in accordance with anembodiment of the present invention. During configuration of thepattern-recognition processor 14, the connections at and between eachlevel of the hierarchy may be programmed in any order. Further, suchconnections may be programmed manually or automatically based on thespecific pattern-matching implementation desired in thepattern-recognition processor 14. It should also be appreciated that theprogramming of the connections at or between levels of the matrix may bedependent on the functionality programmed into other levels of thematrix. Initially, the connections at the first hierarchy level may beprogrammed (block 144). For example, this may include programmingconnections between the output of the feature cells 30 and the localroute lines 102 and the inputs of the feature cells 30 and the localroute lines 102, such as programming the connections 106 described abovein FIG. 17. In some embodiments, the input and/or output connectionsfrom the feature cells 30 may be non-programmable connections and maynot be programmed. For example, as also described above in FIG. 17, inone embodiment the output connections 104 may be non-programmableconnections.

Next, the connections at a second level of the hierarchy may beprogrammed (block 146). In one embodiment such programming may includeprogramming the input connections between the row route lines 108 and agroup 94, as described above in FIG. 18. For example, the connections112 between the input to the feature cells 30 and the row route lines108 may be programmed to be a logical OR (or other function) of theinputs 105 to the feature cells 30. Similarly, the output connections110 may be programmed to provide the desired functionality between therow route lines and the output drive selects 96 of the groups 94.

Additionally, the connections at a third level of the hierarchicalrouting matrix may be programmed (block 148). As discussed above in FIG.19, in one embodiment the connections 116 between the row route lines112 and the intra-block route lines 114 may be programmed. For example,the connections 116 may be programmed to provide the desiredfunctionality between the intra-block route lines 114 and the row routelines 112, to isolate (disable) certain feature cells, or any otherprogrammable function.

Next, the connections at a fourth hierarchy level may be programmed(block 150). In the embodiment depicted above in FIG. 20, suchprogramming may include programming the connections between theintra-block route lines 114 and the top level route lines 118. Forexample, the connections 120 shown above in FIG. 20 may be programmed toprovide the desired functionality between the top level route lines 118and the intra-block route lines 114. As also discussed above in FIG. 21,in some embodiments, such programming may include disabling redundantcapacity (e.g., feature cells) of the pattern-recognition processor 14.As shown in FIG. 22, such programming of the connections may continue upto the nth level of the routing matrix (block 152). By programming theconnections of the routing matrix, the pattern-recognition processor 14may be configured to provide the desired logic and next state routingbetween feature cells (and state machines). As mentioned above, theprogramming of the connections provides knowledge of the configurationof the pattern-recognition processor 14 yet also provides routingflexibility and changeability for different implementations

What is claimed is:
 1. A device, comprising: a logical group comprisinga plurality of route lines, a first feature cell, a second feature cell,and an output drive selector coupled to the first feature cell via afirst non-programmable connection and coupled to the second feature cellvia a second non-programmable connection, wherein the first feature cellis configured to transmit an output signal along a first route line ofthe plurality of route lines to the second feature cell as an inputsignal to the second feature cell, wherein the first feature cell andthe second feature cell of the logical group are separate and distinctfrom any additional feature cells of any other logical group of thedevice, wherein the output drive selector is configured to provide anoutput drive signal of the logical group based upon a selection by theoutput drive selector, wherein the first feature cell comprises a dataanalysis element comprising a memory component programmed withconfiguration data, wherein the data analysis element is configured toanalyze at least a portion of data based on the configuration data andto output a result.
 2. The device of claim 1, wherein each of the firstfeature cell and the second feature cell comprise a respective enableinput and an enable output.
 3. The device of claim 2, wherein the firstfeature cell is configured to transmit the output signal from the enableoutput of the first feature cell to the enable input of the secondfeature cell.
 4. The device of claim 2, comprising a first programmableconnection configured to selectively couple the enable input of thefirst feature cell to the first route line and a second programmableconnection configured to selectively couple the enable input of thefirst feature cell to a second route line of the plurality of routelines.
 5. The device of claim 2, comprising a non-programmableconnection configured to couple the enable output of the first featurecell to the first route line.
 6. The device of claim 1, comprising afirst enable input connection configured to selectively couple thesecond feature cell to the first route line.
 7. The device of claim 6,comprising a second enable input connection configured to selectivelycouple the first feature cell to a second route line of the plurality ofroute lines.
 8. The device of claim 1, comprising a first enable outputconnection configured to couple the first feature cell to the firstroute line.
 9. The device of claim 8, comprising a second enable outputconnection configured to couple the second feature cell to a secondroute line of the plurality of route lines.
 10. The device of claim 1,wherein the output drive selector is configured to provide the outputdrive signal based on the selection of the output signal of the firstfeature cell, an output of the second feature cell, or a logicalcombination of the output signal of the first feature cell and theoutput of the second feature cell.
 11. A device comprising: a firstlogical group comprising a first plurality of route lines, a firstfeature cell, a second feature cell, and a first output drive selectorcoupled to the first feature cell via a first non-programmableconnection and coupled to the second feature cell via a secondnon-programmable connection, wherein the first feature cell isconfigured to transmit a first output signal along a first route line ofthe first plurality of route lines to the second feature cell as aninput signal to the second feature cell, wherein the output driveselector is configured to provide an output drive signal of the firstlogical group based upon a selection by the output drive selector,wherein the first feature cell comprises a data analysis elementcomprising a memory component programmed with configuration data,wherein the data analysis element is configured to analyze at least aportion of data based on the configuration data and to output a result;a second logical group comprising a second plurality of route lines, athird feature cell, a fourth feature cell, and a second output driveselector coupled to the third feature cell via a third non-programmableconnection and coupled to the fourth feature cell via a fourthnon-programmable connection, wherein the third feature cell isconfigured to transmit a second output signal along a first route lineof the second plurality of route lines to the fourth feature cell as aninput signal to the fourth feature cell, wherein the first feature celland the second feature cell of the first logical group are separate anddistinct from the third feature cell and the fourth feature cell of thesecond logical group, wherein the second output drive selector isconfigured to provide a second output drive signal of the second logicalgroup based upon a selection by the second output drive selector. 12.The device of claim 11, wherein the second feature cell is configured totransmit a third output signal based on the first output signal to thefirst output drive selector.
 13. The device of claim 12, wherein thefirst output drive selector is configured to transmit the third outputsignal to a row route line of a plurality of row route lines of thedevice as the output drive signal.
 14. The device of claim 13, whereinthe third feature cell is configured to receive the third output signalfrom the row route line as an input signal to the third feature cell.15. The device of claim 14, wherein the third feature cell is configuredto generate the second output signal based on the third output signal.16. A system, comprising: a plurality of blocks, wherein each block ofthe plurality of blocks comprises a plurality of rows, wherein each rowof the plurality of rows comprises a plurality of logical groups,wherein each logical group comprises: a plurality of route lines; afirst feature cell comprising a first data analysis element comprising afirst plurality of memory cells programmed with first configurationdata, wherein the first data analysis element is configured to analyzeat least a portion of a data stream based on the first configurationdata and to output a first result; a second feature cell comprising asecond data analysis element comprising a second plurality of memorycells programmed with second configuration data, wherein the second dataanalysis element is configured to analyze at least a second portion ofthe data stream based on the second configuration data and to output asecond result; and an output drive selector coupled to the first featurecell via a first non-programmable connection and coupled to the secondfeature cell via a second non-programmable connection, wherein theoutput drive selector is configured to transmit an output drive signalbased upon a selection by the output drive selector.
 17. The system ofclaim 16, wherein the plurality of logical groups of each row of theplurality of rows are interconnected via a plurality of row route lines.18. The system of claim 17, wherein the plurality of rows of each blockof the plurality of blocks are interconnected via a plurality ofintra-block route lines.